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Impact Analysis of Smart Road Stud on Driving Behavior and Traffic Flow in Two-Lane Two-Way Highway

Author

Listed:
  • Maosheng Li

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410083, China)

  • Qian Luo

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410083, China)

  • Jing Fan

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410083, China)

  • Qingyan Ning

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410083, China)

Abstract

The smart road stud (SRS) system can improve the driver’s overtaking behavior through light guidance, which shows great potential in raising the traffic efficiency of two-way two-lane roads (TWTL). In this paper, we propose a light guidance system based on SRS and a combination of driving simulator and microscopic traffic simulation methodologies for evaluating the effect of smart road studs on a TWTL traffic flow. The driving simulation results reveal that SRSs do not only drastically alter microscopic driving characteristics but also it significantly influences drivers’ decision-making process for overtaking. The frequency of overtaking with SRS escalated by 114.58% compared to that without, with the key differential in overtaking decision patterns manifesting predominantly in the selection of distance between oncoming vehicles traveling in the opposite direction. Microsimulation results demonstrate that the implementation of a smart stud system can enhance both the safety and traffic efficiency on the TWTL roadway with limited sight.

Suggested Citation

  • Maosheng Li & Qian Luo & Jing Fan & Qingyan Ning, 2023. "Impact Analysis of Smart Road Stud on Driving Behavior and Traffic Flow in Two-Lane Two-Way Highway," Sustainability, MDPI, vol. 15(15), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11559-:d:1203238
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    References listed on IDEAS

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    1. Ou, Hui & Tang, Tie-Qiao, 2018. "Impacts of moving bottlenecks on traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 131-138.
    2. Zhao, Yongxiang & Zhang, H.M., 2017. "A unified follow-the-leader model for vehicle, bicycle and pedestrian traffic," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 315-327.
    3. Kanagaraj, Venkatesan & Treiber, Martin, 2018. "Self-driven particle model for mixed traffic and other disordered flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1-11.
    4. Castillo, Enrique & Menéndez, José María & Sánchez-Cambronero, Santos, 2008. "Predicting traffic flow using Bayesian networks," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 482-509, June.
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